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Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area

CHEN Shengbo HUANG Shuang LIU Yanli ZHOU Chao

CHEN Shengbo, HUANG Shuang, LIU Yanli, ZHOU Chao. Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area[J]. 中国地理科学, 2018, 28(6): 957-972. doi: 10.1007/s11769-018-1005-z
引用本文: CHEN Shengbo, HUANG Shuang, LIU Yanli, ZHOU Chao. Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area[J]. 中国地理科学, 2018, 28(6): 957-972. doi: 10.1007/s11769-018-1005-z
CHEN Shengbo, HUANG Shuang, LIU Yanli, ZHOU Chao. Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area[J]. Chinese Geographical Science, 2018, 28(6): 957-972. doi: 10.1007/s11769-018-1005-z
Citation: CHEN Shengbo, HUANG Shuang, LIU Yanli, ZHOU Chao. Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area[J]. Chinese Geographical Science, 2018, 28(6): 957-972. doi: 10.1007/s11769-018-1005-z

Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area

doi: 10.1007/s11769-018-1005-z
基金项目: Under the auspices of National Science and Technology Major Project of China (No. 04-Y20A35-9001-15/17), the Program for JLU Science and Technology Innovative Research Team (No. JLUSTIRT, 2017TD-26), the Changbai Mountain Scholars Program, Jilin Province, China
详细信息
    通讯作者:

    CHEN Shengbo.E-mail:chensb0408@126.com

Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area

Funds: Under the auspices of National Science and Technology Major Project of China (No. 04-Y20A35-9001-15/17), the Program for JLU Science and Technology Innovative Research Team (No. JLUSTIRT, 2017TD-26), the Changbai Mountain Scholars Program, Jilin Province, China
More Information
    Corresponding author: CHEN Shengbo.E-mail:chensb0408@126.com
  • 摘要: Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant (SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set's reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis (PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting (SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.
  • [1] Blouin C, Perry S, Lavell A et al., 2009. Reproducing the manual annotation of multiple sequence alignments using a SVM clas-sifier. Bioinformatics, 25(23):3093-3098. doi: 10.1093/bioin-formatics/btp552
    [2] Carranza E J M, Hale M, 2002. Mineral imaging with landsat thematic mapper data for hydrothermal alteration mapping in heavily vegetated terrane. International Journal of Remote Sensing, 23(2):4827-4852. doi: 10.1080/01431160110115014
    [3] Chen G Y, Qian S E, 2011. Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Transactions on Geoscience and Remote Sensing, 49(3):973-980. doi: 10.1109/TGRS.2010.2075937
    [4] Clark R N, 1999. Chapter 1:spectroscopy of rocks and minerals, and principles of spectroscopy. In:Rencz A N (ed). Manual of Remote Sensing, Volume 3, Remote Sensing for the Earth Sci-ences. New York:John Wiley and Sons, 3-58.
    [5] Crowley J K, Hubbard B E, Mars J C, 2003. Analysis of potential debris flow source areas on Mount Shasta, California, by using airborne and satellite remote sensing data. Remote Sensing of Environment, 87(2-3):345-358. doi:10.1016/j. rse.2003.08.003
    [6] Curran P J, Dungan J L, Peterson D L, 2001. Estimating the foliar biochemical concentration of leaves with reflectance spec-trometry:testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76(3):349-359. doi: 10.1016/S0034-4257(01)00182-1
    [7] Di Tommaso I, Rubinstein N, 2007. Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geology Reviews, 32(1-2):275-290. doi: 10.1016/j.oregeorev.2006.05.004
    [8] Eklundh L, Singh A, 1993. A comparative analysis of standardised and unstandardised principal components analysis in remote sensing. International Journal of Remote Sensing, 14(7):1359-1370. doi: 10.1080/01431169308953962
    [9] El Desouky H A, Muchez P, Dewaele S et al., 2008. Postorogenic origin of the stratiform cu mineralization at lufukwe, lufilian foreland, democratic republic of Congo. Economic Geology, 103(3):555-582. doi: 10.2113/gsecongeo.103.3.555
    [10] Gabr S, Ghulam A, Kusky T, 2010. Detecting areas of high-po-tential gold mineralization using ASTER data. Ore Geology Reviews, 38(1-2):59-69. doi:10.1016/j.oregeorev.2010.05. 007
    [11] Galvão L S, Almeida-Filho R, Vitorello Í, 2005. Spectral dis-crimination of hydrothermally altered materials using ASTER short-wave infrared bands:evaluation in a tropical savannah environment. International Journal of Applied Earth Observa-tion and Geoinformation, 7(2):107-114. doi:10.1016/j.jag. 2004.12.003
    [12] Gong P, Pu R L, Biging G S et al., 2003. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 41(6):1355-1362. doi:10.1109/TGRS.2003. 812910
    [13] Green A A, Craig M D, 1985. Analysis of aircraft spectrometer data with logarithmic residuals. In:Proceedings of the 3rd Airborne Imaging Spectrometer Data Analysis Workshop. Pasadena, USA:JPL Publication, 111-119.
    [14] Hu Bo, Zhu Guchang, Zhang Yuanfei et al., 2011. The application of spatial U-static method to the extraction of alteration anom-alies. Remote Sensing for Land & Resources, (3):71-76. (in Chinese)
    [15] Hubbard B E, Crowley J K, Zimbelman D R, 2003. Comparative alteration mineral mapping using visible to shortwave infrared (0.4-2.4μm) Hyperion, ALI, and ASTER imagery. IEEE Transactions on Geoscience and Remote Sensing, 41(6):1401-1410. doi: 10.1109/TGRS.2003.812906
    [16] Hubbard B E, Crowley J K, 2005. Mineral mapping on the Chil-ean-Bolivian Altiplano using co-orbital ALI, ASTER and Hy-perion imagery:data dimensionality issues and solutions. Re-mote Sensing of Environment, 99(1-2):173-186. doi:10. 1016/j.rse.2005.04.027
    [17] Hunt G R, 1977. Spectral signatures of particulate minerals in the visible and near infrared. Geophysics, 42(3):501-513. doi: 10.1190/1.1440721
    [18] Ju J C, Kolaczyk E D, Gopal S, 2003. Gaussian mixture discri-minant analysis and sub-pixel land cover characterization in remote sensing. Remote Sensing of Environment, 84(4):550-560. doi: 10.1016/S0034-4257(02)00172-4
    [19] Landgrebe D, 2002. Hyperspectral image data analysis. IEEE Signal Processing Magazine, 19(1):17-28. doi: 10.1109/79.974718
    [20] Manolakis D, Marden D, Shaw G A, 2003. Hyperspectral image processing for automatic target detection applications. Lincoln Laboratory Journal, 14(1):79-116.
    [21] Massironi M, Bertoldi L, Calafa P et al., 2008. Interpretation and processing of ASTER data for geological mapping and granit-oids detection in the Saghro massif (eastern Anti-Atlas, Mo-rocco). Geosphere, 4(4):736-759. doi: 10.1130/GES00161.1
    [22] Mikucki E J, Ridley J R, 1993. The hydrothermal fluid of Ar-chaean lode-gold deposits at different metamorphic grades:compositional constraints from ore and wallrock alteration as-semblages. Mineralium Deposita, 28(6):469-481. doi:10. 1007/BF02431603
    [23] Pour A B, Hashim M, 2011. Identification of hydrothermal altera-tion minerals for exploring of porphyry copper deposit using ASTER data, SE Iran. Journal of Asian Earth Sciences, 42(6):1309-1323. doi: 10.1016/j.jseaes.2011.07.017
    [24] Pour A B, Hashim M, 2012a. The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore Geology Reviews, 44:1-9. doi:10.1016/j.oregeorev. 2011.09.009
    [25] Pour A B, Hashim M, 2012b. Identifying areas of high econom-ic-potential copper mineralization using ASTER data in the Urumieh-Dokhtar Volcanic Belt, Iran. Advances in Space Re-search, 49(4):753-769. doi: 10.1016/j.asr.2011.11.028
    [26] Pour A B, Hashim M, 2013. Fusing ASTER, ALI and Hyperion data for enhanced mineral mapping. International Journal of Image and Data Fusion, 4(2):126-145. doi:10.1080/194 79832.2012.753115
    [27] Pour A B, Hashim M, van Genderen J, 2013. Detection of hydro-thermal alteration zones in a tropical region using satellite re-mote sensing data:Bau goldfield, Sarawak, Malaysia. Ore Ge-ology Reviews, 54:181-196. doi:10.1016/j.oregeorev.2013. 03.010
    [28] Pour A B, Hashim M, Marghany M, 2014. Exploration of gold mineralization in a tropical region using Earth Observing-1 (EO1) and JERS-1 SAR data:a case study from Bau gold field, Sarawak, Malaysia. Arabian Journal of Geosciences, 7(6):2393-2406. doi: 10.1007/s12517-013-0969-3
    [29] Pour A B, Hashim M, 2014. ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration. SpringerPlus, 3:130. doi: 10.1186/2193-1801-3-130
    [30] Rajendran S, Al-Khirbash S, Pracejus B et al., 2012. ASTER de-tection of chromite bearing mineralized zones in Semail Ophi-olite Massifs of the northern Oman Mountains:exploration strategy. Ore Geology Reviews, 44:121-135. doi:10. 1016/j.oregeorev.2011.09.010
    [31] Sabins F F, 1999. Remote sensing for mineral exploration. Ore Geology Reviews, 14(3-4):157-183. doi: 10.1016/S0169-1368(99)00007-4
    [32] Sillitoe R H, Hedenquist J W, 2003. Volcanic, geothermal and ore-forming fluids:Rulers and witnesses of processes within the Earth:Linkages between volcanotectonic settings, ore-fluid compositions, and epithermal precious metal deposits. Society of Economic Geologists and Geochemical Society Special Publications, 315-343.
    [33] Sims D A, Gamon J A, 2003. Estimation of vegetation water con-tent and photosynthetic tissue area from spectral reflectance:a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment, 84(4):526-537. doi: 10.1016/S0034-4257(02)00151-7
    [34] Stoner E R, Baumgardner M F, 1981. Characteristic variations in reflectance of surface soils. Soil Science Society of America, 45(6):1161-1165. doi:10.2136/sssaj1981.036159950045000 60031x
    [35] van der Meer F, 2004. Analysis of spectral absorption features in hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 5(1):55-68. doi:10.1016/j. jag.2003.09.001
    [36] Vaughan R G, Calvin W M, Taranik J V, 2003. Sebass hyperspec-tral thermal infrared data:surface emissivity measurement and mineral mapping. Remote Sensing of Environment, 85(1):48-63. doi: 10.1016/S0034-4257(02)00186-4
    [37] Vicente L E, de Souza Filho C R, 2011. Identification of mineral components in tropical soils using reflectance spectroscopy and advanced spaceborne thermal emission and reflection radiometer (ASTER) data. Remote Sensing of Environment, 115(8):1824-1836. doi: 10.1016/j.rse.2011.02.023
    [38] Wei Hao, Xu Jiuhua, Zeng Qingdong et al., 2011. Fluid evolution of alteration and mineralization at the Duobaoshan porphyry Cu (Mo) deposit, Heilongjiang Province. Acta Petrologica Sinica, 27(5):1361-1374. (in Chinese)
    [39] Yang J, Sun J, Ge Q et al., 2017. Assessing the impacts of urbanization-associated green space on urban land surface tem-perature:a case study of Dalian, China. Urban Forestry & Urban Greening. 22:1-10. doi:10.1016/j.ufug.2017. 01.002.
    [40] Yang Z A, Peng S L, Zhu G C et al., 2009. Spectrum spatial structure characteristic analysis of remote sensing alteration information and interference factors. Journal of Central South University of Technology, 16(4):647-652. doi:10.1007/s117 71-009-0107-2
    [41] Zhang Yuanfei, Yuan Jiming, Zhu Guchang et al., 2010. A study of spatial structure analysis and alteration information extraction based on random models of remote sensing data. Remote Sensing for Land & Resources, (4):34-39. (in Chinese)
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Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area

doi: 10.1007/s11769-018-1005-z
    基金项目:  Under the auspices of National Science and Technology Major Project of China (No. 04-Y20A35-9001-15/17), the Program for JLU Science and Technology Innovative Research Team (No. JLUSTIRT, 2017TD-26), the Changbai Mountain Scholars Program, Jilin Province, China
    通讯作者: CHEN Shengbo.E-mail:chensb0408@126.com

摘要: Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant (SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set's reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis (PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting (SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.

English Abstract

CHEN Shengbo, HUANG Shuang, LIU Yanli, ZHOU Chao. Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area[J]. 中国地理科学, 2018, 28(6): 957-972. doi: 10.1007/s11769-018-1005-z
引用本文: CHEN Shengbo, HUANG Shuang, LIU Yanli, ZHOU Chao. Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area[J]. 中国地理科学, 2018, 28(6): 957-972. doi: 10.1007/s11769-018-1005-z
CHEN Shengbo, HUANG Shuang, LIU Yanli, ZHOU Chao. Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area[J]. Chinese Geographical Science, 2018, 28(6): 957-972. doi: 10.1007/s11769-018-1005-z
Citation: CHEN Shengbo, HUANG Shuang, LIU Yanli, ZHOU Chao. Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area[J]. Chinese Geographical Science, 2018, 28(6): 957-972. doi: 10.1007/s11769-018-1005-z
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